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Analyzing prediction market trading behaviour to select Delphi-experts

Analyzing prediction market trading behaviour to select Delphi-experts The selection of experts for Delphi studies is crucial for the quality of the forecast results and the information taken into account. In the past, this has usually been done by selecting participants according to their reputation, although this approach is questionable in terms of reaching the most knowledgeable participants having new, relevant and valid information. In this context, this paper aims to propose to operate a prediction market alongside Delphi studies and select participants based on their trading behaviour in the market for the Delphi study.Design/methodology/approachBased on more than three years of historical prediction market trading data, the authors verify attributes that indicate insightful trades, as previously discussed in the finance literature, by using regression and classification trees.FindingsThe paper contributes attributes of trading behaviour that are theoretically derived from literature and potentially related to informed traders. These are tested and evaluated on historical prediction market data. Especially, the trading volume, the spread at the moment of trading and the market maker attribute seem to predict informed traders the best.Originality/valueAlgorithms based on identified attributes can be used to objectify the selection of experts for Delphi studies with potential gains in terms of the amount of information considered. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png foresight Emerald Publishing

Analyzing prediction market trading behaviour to select Delphi-experts

foresight , Volume 20 (4): 11 – Oct 30, 2018

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1463-6689
DOI
10.1108/fs-01-2018-0009
Publisher site
See Article on Publisher Site

Abstract

The selection of experts for Delphi studies is crucial for the quality of the forecast results and the information taken into account. In the past, this has usually been done by selecting participants according to their reputation, although this approach is questionable in terms of reaching the most knowledgeable participants having new, relevant and valid information. In this context, this paper aims to propose to operate a prediction market alongside Delphi studies and select participants based on their trading behaviour in the market for the Delphi study.Design/methodology/approachBased on more than three years of historical prediction market trading data, the authors verify attributes that indicate insightful trades, as previously discussed in the finance literature, by using regression and classification trees.FindingsThe paper contributes attributes of trading behaviour that are theoretically derived from literature and potentially related to informed traders. These are tested and evaluated on historical prediction market data. Especially, the trading volume, the spread at the moment of trading and the market maker attribute seem to predict informed traders the best.Originality/valueAlgorithms based on identified attributes can be used to objectify the selection of experts for Delphi studies with potential gains in terms of the amount of information considered.

Journal

foresightEmerald Publishing

Published: Oct 30, 2018

Keywords: Delphi method; Crowd sourcing; Expert selection; Prediction market; Real-time Delphi; Trading behaviour analysis

References